Turbidity Inversion from the ADCP Using a CNN-ResNet-RF Hybrid Model

Addressing the limitations of traditional acoustic turbidity inversion models in complex marine environments—specifically their reliance on empirical parameters and lack of vertical resolution—this study presents a novel CNN-ResNet-RF hybrid model based on the simultaneous ADCP and turbidity observations in the Chengshantou sea area. Unlike conventional approaches, the proposed framework integrates deep spatio-temporal features automatically extracted by a ResNet-enhanced CNN, utilizing a Random Forest (RF) regressor for final prediction, thereby avoiding the limitations of artificial feature engineering. To ensure rigorous evaluation and mitigate stochastic bias, the model was validated using a 5-fold cross-validation strategy with dynamic Z-score normalization. Experimental results demonstrate that the proposed model significantly outperforms benchmark methods (CNN, RF, and CNN-RF), achieving an average R2 of 0.782, an MAE of 4.454, and a MAPE of 15.42% on the test sets. This study confirms that the hybrid framework successfully combines the feature extraction power of deep learning with the robustness of ensemble learning, providing a robust and high-precision tool for the vertical structural analysis of ocean turbidity.